Update app.py

app.py

@@ -1,124 +1,92 @@
-import nltk
-from spacy.lang.en import English
-
-# Example input: process description
-process_description = """
-The accounts payable team receives invoices via email. 
-They verify the invoice details, check for duplicates, and approve payment.
-"""
-
-# Preprocess the text
-def preprocess_text(text):
-    tokenizer = English()
-    tokens = tokenizer(text)
-    processed_text = [token.lemma_ for token in tokens if not token.is_stop]
-    return ' '.join(processed_text)
-
-processed_desc = preprocess_text(process_description)
-print(processed_desc)
-
-
+# RPA Business Analyst AI Agent Implementation
+# pip install pm4py neo4j rasa opencv-python pytesseract tensorflow scikit-learn
+
+# imports
+import pm4py
+import cv2
+from mss import mss
+import pytesseract
 import spacy
-
-nlp = spacy.load('en_core_web_sm')
-
-def extract_entities(text):
-    doc = nlp(text)
-    entities = [(ent.text, ent.label_) for ent in doc.ents]
-    return entities
-
-entities = extract_entities(process_description)
-print("Extracted Entities:", entities)
-
-
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.svm import SVC
-
-# Sample training data (simplified)
-X = [
-    "receive invoices via email",  # Automatable
-    "verify invoice details",       # Automatable
-    "approve payment manually"      # Non-automatable
-]
-y = [1, 1, 0]
-
-# Feature extraction
-vectorizer = TfidfVectorizer()
-X_vec = vectorizer.fit_transform(X)
-
-# Train a simple SVM
-model = SVC()
-model.fit(X_vec, y)
-
-# Predict automation feasibility
-def predict_automation_feasibility(text):
-    text_vec = vectorizer.transform([text])
-    return model.predict(text_vec)[0]
-
-print(predict_automation_feasibility("check for duplicates"))  # Output: 1 (Automatable)
-
-
-# Example workflow for UiPath
-def generate_uipath_workflow(tasks):
-    workflow = f"""
-    <Workflow [ContentUIVersion='1.0.0.0' TargetPlatform='.NETFramework,Version=v6.0' TargetRuntime='V6_0' HostRuntimeERO='255,255'>
-        <Variable Type='Object' Name='invoiceDetails' />
-        {''.join([f"<Variable Type='Object' Name='task_{task}' />" for task in tasks])}
-        <Sequence>
-            {''.join([f"<Activitysqueeze Code='GeneratedActivity严格落实任务_{task}' />" for task in tasks])}
-        </Sequence>
-    </Workflow>
-    """
-    return workflow
-
-tasks = ["receive_invoices", "verify_details", "approve_payment"]
-workflow = generate_uipath_workflow(tasks)
-print(workflow)
-
-
-# Example workflow for UiPath
-def generate_uipath_workflow(tasks):
-    workflow = f"""
-    <Workflow [ContentUIVersion='1.0.0.0' TargetPlatform='.NETFramework,Version=v6.0' TargetRuntime='V6_0' HostRuntimeERO='255,255'>
-        <Variable Type='Object' Name='invoiceDetails' />
-        {''.join([f"<Variable Type='Object' Name='task_{task}' />" for task in tasks])}
-        <Sequence>
-            {''.join([f"<Activitysqueeze Code='GeneratedActivity严格落实任务_{task}' />" for task in tasks])}
-        </Sequence>
-    </Workflow>
-    """
-    return workflow
-
-tasks = ["receive_invoices", "verify_details", "approve_payment"]
-workflow = generate_uipath_workflow(tasks)
-print(workflow)
-
-
-
-# Example: Connect to UiPath Orchestrator API
-import requests
-
-def execute_workflow(workflow, uipath_uri, api_key):
-    headers = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/xml"
-    }
-    response = requests.post(f"{uipath_uri}/api/workflows", headers=headers, data=workflow)
-    return response.json()
-
-# Example API call
-uipath_uri = "https://your-uipath-orchestrator-url"
-api_key = "your-api-key"
-
-response = execute_workflow(workflow, uipath_uri, api_key)
-print("Workflow Execution Response:", response)
-
-
-
-
-
-
-
-
-
-
+from neo4j import GraphDatabase
+import numpy as np
+import pandas as pd
+import json
+import tensorflow as tf
+from sklearn.ensemble import GradientBoostingRegressor
+
+# Process Mining Module
+def discover_process(event_log_path, algorithm="inductive"):
+    log = pm4py.read_xes(event_log_path)
+    if algorithm == "inductive":
+        model = pm4py.discover_petri_net_inductive(log)
+    else:
+        model = pm4py.discover_petri_net_alpha(log)
+    pm4py.view_petri_net(*model)
+    return model
+
+# Computer Vision Module
+class ScreenCapture:
+    def capture_screen(self):
+        with mss() as sct:
+            monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}
+            img = sct.grab(monitor)
+            return cv2.cvtColor(np.array(img), cv2.COLOR_BGRA2BGR)
+    
+    def extract_text(self, image):
+        return pytesseract.image_to_string(image)
+
+# NLP Module
+nlp_model = spacy.load("en_core_web_sm")
+def parse_workflows(text):
+    doc = nlp_model(text)
+    return [ent.text for ent in doc.ents if ent.label_ == "WORKFLOW"]
+
+# Requirements Analysis Module
+driver = GraphDatabase.driver("bolt://localhost", auth=None)
+def create_process_node(process_name, process_description):
+    with driver.session() as session:
+        session.run(
+            "MERGE (p:Process {name: $name}) SET p.description = $description",
+            name=process_name,
+            description=process_description
+        )
+
+# Automation Opportunity Detector
+class SuitabilityPredictor:
+    def __init__(self, model_path):
+        self.model = tf.keras.models.load_model(model_path)
+    
+    def predict(self, features):
+        return self.model.predict(features)[0][0]
+
+def automate_suitability(process_data, model_path):
+    predictor = SuitabilityPredictor(model_path)
+    return predictor.predict(process_data)
+
+# Main Execution Block
+if __name__ == "__main__":
+    # Load Configuration
+    with open("config.json") as f:
+        config = json.load(f)
+    
+    # Process Mining
+    event_log_path = "data/event_logs.xes"
+    process_model = discover_process(event_log_path, config["process_mining"]["algorithm"])
+    
+    # Computer Vision
+    screen_cap = ScreenCapture()
+    screen_img = screen_cap.capture_screen()
+    screen_text = screen_cap.extract_text(screen_img)
+    
+    # NLP
+    workflows = parse_workflows(screen_text)
+    
+    # Knowledge Graph
+    create_process_node("Invoice Processing", "Processes vendor invoices")
+    
+    # ML Suitability
+    model_path = config["ml"]["model_path"]
+    suitability_score = automate_suitability(process_data, model_path)
+    
+    # Recommendation Engine
+    print(f"Automation Suitability: {suitability_score:.2f}")